通过自然对话的照顾者反来模拟儿童的语法学习
Mitja Nikolaus1, Abdellah Fourtassi2
1Centre de Recherche Cerveau et Cognition, Université de Toulouse, 31059 Toulouse, France.
概括
护理人员的反,特别是澄清请求,语言模型中的语法改进. 然而,这并没有提高复杂语法评估的表现,这表明需要在语言学习研究中使用多种类型的反.
科学领域:
- 认知科学 认知科学
- 发展心理学 发展心理学
- 计算语言学 计算语言学
背景情况:
- 消极证据在语言学习中的作用仍在争论中.
- 传统的方法很难将护理人员的反与自然的社会互动隔离开来.
- 计算建模为研究这个问题提供了一种新的方法.
研究的目的:
- 调查护理人员的反是否能提高语法学习,超出一般输入.
- 模拟特定反类型,如澄清请求,对语法学习的影响.
- 引入一个灵活的计算框架,用于评估语言发展中的各种反机制.
主要方法:
- 训练有素的语言模型在儿童指导的语言体上.
- 微调的模型使用强化学习与护理人员类似的反 (澄清请求).
- 对比语法知识基准的语法输出和表现.
主要成果:
- 微调模型产生了比基线模型更多的语法发言.
- 在具有挑战性的语法评估基准上没有观察到显著的改善.
- 通过额外的反类型证明了改善基准的潜力.
结论:
- 护理人员的澄清请求可以改善模型中的语法生产.
- 当前的语法评估基准可能无法完全捕捉来自反的学习.
- 拟议的计算框架有助于对社会互动和语言发展进行更广泛的研究.
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